Exploring the Lesser-Known Bioactive Natural Products of Plant Species of the Genus Cannabis L.: Alkaloids, Phenolic Compounds, and Their Therapeutic Potential
Bibliographic record
Abstract
Plant species of the genus Cannabis L. are predominantly recognized for their cannabinoids, which have garnered significant attention due to their bioactive properties. However, Cannabis also produces a diverse array of bioactive compounds with promising pharmacological potential that remain underexplored. This review focuses primarily on phytochemicals derived from Cannabis sativa L. subspecies, including both its drug-type and fiber-type varieties, which are the most widely cultivated and studied within the genus. Among these, nitrogen-containing compounds such as spermidine alkaloids exhibit neuroprotective and anti-aging properties, while hydroxycinnamic acids and hydroxycinnamic acid amides, including N-trans-caffeoyltyramine and N-trans-feruloyltyramine, have demonstrated notable antioxidant and anti-inflammatory activities. Additionally, Cannabis species are a valuable source of unique stilbenes, such as canniprene, and flavonoids, including cannflavin A and B, which demonstrated potent anti-inflammatory and antiproliferative effects. Despite this rich phytochemical diversity, research on these compounds remains limited, largely due to historical legal restrictions. This literature review consolidates and updates current knowledge on these lesser-studied phytochemicals of Cannabis, detailing their biosynthetic pathways, metabolic precursors, and emerging therapeutic applications. By expanding the research focus beyond cannabinoids, this work aims to enhance our understanding of Cannabis’s full pharmacological potential and promote further investigation into its diverse chemical constituents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".